CRII: Modeling Student Knowledge and Performance When Learning from Multiple Types of Materials

NSF Grant No. 1755910

PI: Shaghayegh (Sherry) Sahebi

Email: ssahebi [at] albany [dot] edu

Project Duration: 9/1/2018 - 8/31/2020

As the national interest in higher and professional education has been increasing, interest in online learning systems has also grown rapidly. Online learning systems aim to contribute to the society by providing high quality, affordable, and accessible education, at scale. Delivering such high-impact goals requires automatic tools that can help us understand students’ learning process and answer questions such as what knowledge is gained by watching a video lecture (domain knowledge modeling), what is a student’s state of knowledge (student knowledge modeling), and how a specific student would perform on a test (predicting student performance). In this project our goal is to achieve a better understanding of students’ learning process in online educational systems, when learning from different learning material types, such as videos, quizzes, etc. To do this, we model student knowledge growth as they interact with all types of learning materials. We develop multi-view machine learning algorithms that minimize the error of student performance prediction while maximizing the correlations among multiple views to the learning data. We evaluate our student models by measuring how we can predict students’ performance on their next quiz or problem.

Related Publications

• C. Wang, S. Zhao, and S. Sahebi “Learning from non-assessed resources: Deep multi-type knowledge tracing”, The 14th International Conference on Educational Data Mining (EDM-21), 2021. paper code

• C. Wang, S. Sahebi, S. Zhao, P. Brusilovsky, and L. Moraes “Knowledge tracing for complex problem solving: Granular rank-based tensor factorization”, The 29th Conference on User Modeling, Adaptation and Personalization (UMAP-21), 2021. paper code

• T.N. Doan, and S. Sahebi, “Transcrosscf: Transition-based cross-domain collaborative filtering”, 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020 paper code

• S. Zhao, C. Wang, and S. Sahebi, “Modeling Knowledge Acquisition from Multiple LearningResource Types”, 13th International Conference on Educational Data Mining (EDM), 2020. paper code video

• M. Mirzaei, S. Sahebi, and P. Brusilovsky, “SB-DNMF: A structure based discriminative non-negativematrix factorization model for detecting inefficient learning behaviors”, The 2020 IEEE/WIC/ACMInternational Joint Conference On Web Intelligence And Intelligent Agent Technology. WI-IAT, 2020. paper code video

• M. Mirzaei, S. Sahebi, , and P. Brusilovsky, “Detecting Trait versus Performance Student Behavioral Pattern Using Discriminative Non-Negative Matrix Factorization”, The Thirty-Third International FLAIRS Conference, 2020. paper code

• T.N. Doan, and S. Sahebi, “Rank-Based Tensor Factorization for Student Performance Prediction”, 12th International Conference on Educational Data Mining (EDM), 2019. paper slides code

• M. Mirzaei, S. Sahebi, , and P. Brusilovsky, “Annotated Examples and Parameterized Exercises: Analyzing Student’s Sequential Patterns”, The 20th International Conference on Artificial Intelligence in Education (AIED), 2019. paper slides

This material is based upon work supported by the National Science Foundation under Grant No. 1755910.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.